According to DB-engines Rankings, MySQL is the second most popular database. On the other hand, BigQuery is Google’s cost-effective, serverless, multi cloud enterprise data warehouse. Copying data from MySQL to BigQuery may be part of your overall data integration strategy, that offers a plethora of advantages, and leveraging one of the powerful data integration tools like Airbyte eases the process even more! Airbyte is like a data engineer's secret weapon! With its powerful capabilities, you can effortlessly connect MySQL to BigQuery that will provide you with the following benefits:

1. Fast analytics without impacting operational workloads: BigQuery is designed for fast and efficient analytics. Furthermore, by creating a copy of your operational data, you can execute complex analytical queries without impacting your operational workloads.
2. Creating a single source of truth and optimize reporting workflows: It can be time-consuming and challenging for analysts to work with multiple platforms. Combining data into a centralized data warehouse reduces this workload by serving as a single source of truth.
3. Improved security: Replicating data out of MySQL into an analytical system such as BigQuery removes the need to grant permissions to data analysts on operational systems, which can improve security.
4. Data insights that go deeper: MySQL is built for OLTP (Online Transaction Processing) operations well, while BigQuery handles OLAP (Online Analytical Processing). BigQuery is a serverless, cost-effective, multi-cloud data warehouse that can help you quickly turn big data into valuable insights.

This tutorial will show you how to use Airbyte cloud, which is a data integration platform, to set up a data replication pipeline from MySQL to BigQuery. You can also checkout another helpful article that takes you through the step-by-step process of connecting MySQL to Snowflake.

Let's get started!
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Prerequisites

To follow the steps provided in this tutorial article you will make use of the following tools.

• Google cloud
• Airbyte cloud
• MySQL database
• MySQL workbench
• Azure Cloud

In this tutorial, you will set up the MySQL database on Microsoft Azure cloud. This is because the Airbyte cloud will make use of a public (not local) IP address to access MySQL. On the other hand, If you want to connect Airbyte to a local instance of MySQL, you may consider downloading and running Airbyte open-source.

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Methods to Move Data From MySQL to BigQuery

• Method 1: Connecting MySQL to BigQuery using Airbyte.
• Method 2: Connecting MySQL to BigQuery manually.

Method 1: Connecting MySQL to BigQuery using Airbyte

Step 1: Set up MySQL as an Airbyte source

Create a MySQL server on the Azure cloud platform, and follow the Quickstart: Create an Azure Database for MySQL server by using the Azure portal.

ℹ️ We use the Azure cloud platform for this tutorial, but you can set up MySQL on any cloud platform you prefer.

Once the server set-up process is complete, open the server dashboard. On the side panel, click the connect button to open up the connection settings and copy the details. We will use them to connect with MySQL workbench, which will serve as the client from which we will build a test database with sample data.

Step 2: Connect MySQL workbench to MySQL server

Open your MySQL workbench and follow the steps to connect it to MySQL server, as instructed in: Quickstart: Use MySQL Workbench to connect and query data in Azure Database for MySQL.

If the above configuration is successful, your MySQL workbench is connected to your instance of MySQL. You can create and access some test data, which will be used to demonstrate Airbyte replication to BigQuery.

Step 3: Set up the MySQL database as a data source on Airbyte Cloud

Log in to your airbyte cloud account, click on the sources tab, and then the +new source button to create a new source.  On the Source type, select MySQL, then complete the details to match the settings you defined when you set up MySQL. This should look similar to the image shown below:

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Step 4: Set up BigQuery

To set up BigQuery, access your account on the google developer console and create a new project. In your newly created project dashboard, click on the navigation menu on the right side and select “IAM & ADMIN” - Service Accounts.

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Click on Create Service Account to create a service account. In the setup window, define your service account name, ID and click create to complete the setup process.

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Setup the storage object admin role. On the Roles’ setup prompt that follows, search for cloud storage service and then select Storage Object Admin role. Click on done to finish the setup process.

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Create a Cloud Storage bucket that will hold your data on BigQuery. Follow the steps below to create a storage bucket. On your project dashboard, search for Cloud Storage on the search bar and select it. Next, click the Create Bucket button to set up your storage bucket. Then provide the following information:

1. Bucket Name. Enter a name for your bucket.
2. Your data Location: The default option is US (multiple Regions in US).
3. Your data storage class: Select the default standard option.
4. Access control: Select the default Uniform option.
5. Data Protection: Select the default none option.

Finally, click on create to finish the setup process. Follow the steps below to create an HMAC key access ID and Secret, which will be used later in Airbyte. After creating your storage bucket, navigate to the bucket dashboard and click on the settings option followed by Interoperability.

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Click on create a key for a service account to select the service account initially created.

Finally, click on Create key to create your HMAC Access Key and Secret.

Copy the HMAC access key and secret. You will use it in the last step of setting up a connection between Airbyte cloud and BigQuery.

Step 5: Set up BigQuery as an airbyte destination

Log in to your Airbyte cloud account to set up the connection to the destination. Click Destinations and then click + New destination and select BigQuery as the destination to start the destination setup prompt. Configure the following details to set up the destination on airbyte:

1. Destination type: BigQuery.
2. Destination name: BigQuery.
3. Project ID: The project ID you initially set when creating your project on the Google console.
4. Dataset location: Select the location of your BigQuery dataset.
5. Dataset Id: Your dataset ID.
6. Loading method: Airbyte recommends GCS staging.
7. Service Account Key JSON. Enter the Google Cloud Service Account Key in JSON format.
8. Transformation Query Run Type: Set the default interactive to have BigQuery run interactive query jobs.
9. Google BigQuery Client Chunk Size: Set the default value of 15 MiB.

This should look similar to the following:

Click on the setup connection button, at which point the connection to the BigQuery destination will be verified.  

Step 5: Sync data from MySQL to BigQuery using Airbyte Cloud

The last step in this process is establishing a connection between the source and destination. Click on Connections on the left tab, and then click on + New connection. Then select the MySQL source that you just created, followed by the new BigQuery destination. At this point you should see a screen similar to the following:

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Give the new connection a name, and define the replication frequency. At the bottom of the above screen, you will see a list of the tables (not shown) that are available in your source, and can select the Sync mode for each one.  If you are not sure which Sync mode (a.k.a. replication mode) to choose, you may wish to read our blog on replication modes.

Once you start the first sync, your data from MySQL will be replicated to BigQuery

Log in to your Google developer console and navigate to your project to see the results of the first sync. In the image below, data has been replicated from a vehicles table into BigQuery – but the data you see will depend on your MySQL source data, and the tables that you have chosen to replicate.

Step 6: Wrapping up

In this step to step tutorial, you have learned how to:

1. Configure a MySQL data source
2. Configure BigQuery as a destination
3. Create an Airbyte cloud connection that syncs data from MySQL to BigQuery

Airbyte’s data integration platform makes it easy to move data from across your enterprise into a single source of truth that is optimized for analytics! This has several benefits including: improved analytics, a single source of truth, improved security, and better data insights. If you have enjoyed this tutorial, you may check out Airbyte's tutorials and Airbyte’s blog to learn more about the platform. You can also join the conversation on our community Slack Channel, participate in discussions on Airbyte’s discourse, or sign up for our newsletter.

Method 2: Connecting MySQL to BigQuery manually.

Moving data from MySQL to Google BigQuery without using third-party connectors or integrations involves several steps, primarily exporting data from MySQL, preparing it for BigQuery, and then importing it into BigQuery. Here is a detailed step-by-step guide:

Step 1: Export Data from MySQL

1. Identify the Data to Export: Decide which tables or data sets you need to export from MySQL.

2. Choose Export Format: BigQuery supports CSV, JSON, Avro, and Parquet formats. Choose a format that suits your needs (CSV is commonly used for simplicity).

3. Export the Data:

    - Connect to your MySQL database using a command-line tool or a database management tool like phpMyAdmin.

    - Use the `mysqldump` command to export your data. For example, to export a table to a CSV file, you can use:

      ```sh

      SELECT * FROM your_table_name

      INTO OUTFILE '/path_to_export/your_table_name.csv'

      FIELDS TERMINATED BY ','

      ENCLOSED BY '"'

      LINES TERMINATED BY '\n';

      ```

    - Make sure that the account used to run the `mysqldump` command has the necessary permissions to access the data and write to the file system.

Step 2: Prepare Data for BigQuery

1. Clean and Format Data: Ensure that the data is clean (e.g., no null bytes, properly escaped newlines, etc.) and conforms to BigQuery’s data types and format requirements.

2. Split Large Files: If you have very large CSV files, consider splitting them into smaller chunks to make the upload process more manageable and to avoid timeouts.

3. Compress Files (Optional): Compress the CSV files using GZIP to reduce upload time and storage costs in BigQuery.

Step 3: Upload Data to Google Cloud Storage (GCS)

1. Create a Google Cloud Storage Bucket:

    - Go to the Google Cloud Console.

    - Navigate to "Storage" and create a new bucket.

    - Set the storage class and location according to your needs.

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2. Upload Files to GCS:

    - Use the `gsutil cp` command to upload your files to the GCS bucket:

      ```sh

      gsutil cp /path_to_export/*.csv gs://your-bucket-name/

      ```

    - Ensure that you have the necessary permissions to upload files to the GCS bucket.

Step 4: Import Data into BigQuery

1. Create a Dataset in BigQuery:

    - Go to the BigQuery console.

    - Create a new dataset where you will store your imported tables.

2. Create Table Schema:

    - Define the schema for your BigQuery table, matching the structure of the MySQL data you exported.

    - You can create the schema manually in the BigQuery UI or define it in a JSON file.

3. Load Data from GCS to BigQuery:

    - In the BigQuery UI, navigate to your dataset and click on "Create table".

    - Set the "Create table from" option to "Google Cloud Storage" and provide the path to your CSV files in the bucket.

    - Choose the file format and specify the schema.

    - Configure additional settings like field delimiter, encoding, etc., as per your CSV format.

    - Click on "Create table" to start the import process.

4. Verify Data Import:

    - Once the data is imported, run some queries to ensure that it looks correct and matches the source data in MySQL.

Step 5: Clean Up

1. Remove Temporary Files:

    - After confirming the successful import, you can delete the exported files from your local system and the GCS bucket to avoid unnecessary storage charges.

2. Monitor Cost and Performance:

    - Check the BigQuery billing and performance to understand the cost implications of the data import and subsequent queries.

By following these steps, you can move data from MySQL to BigQuery without using third-party connectors or integrations. Keep in mind that depending on the size of your data and the resources available, some steps might take longer, and you might need to adjust the process to fit your specific needs.

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